Yearly mean SST

A trend analysis forthe investigated period reflects that the seven warmest years were observed since 1999. The positive trend in the yearly mean SSTs with an increase of 0.97K in 16 years (0.65K/decade) confirms the global observation also for the Baltic Sea. Summer and autumn temperatures mostly contribute to this positive trend. The monthly resolved trend varies between the slightly negative trends in March and the highest positive trends in July in the northern Baltic Sea. The yearly trend of the entire Baltic Sea is stronger than that of the northern hemisphere. 

FIGURE 9.4 Yearly mean SST along the transect through the Baltic Sea for all years of the investigation period with marked 15 years mean, and the coldest and warmest years. 

FIGURE 9.5 Anomalies of the yearly mean SST of the Baltic Sea in the period 1990-2005. 

The yearly mean SSTs of the Baltic Sea of the investigation period from 1990 to 2005 show a slight positive trend in the mean temperature development in the entire Baltic Sea with an increase of approximately 0.97K in the period of 16 years (Fig. 9.6). This trend corresponds to the findings in the EEA Report (2004) with an increase of 0.5K in the past 15 years. 

Fig. 2.25 Temporal mean (left) and standard deviation (right) of the zonal mean volatilisation rate over 10 years [kg/(kg s)]. Dashed lines show volatilisation rates derived from zonal mean SST and wind speed (denoted as zonally averaging model). Solid lines show volatilisation rates derived from zonally resolved SST and wind speed, which were zonally averaged afterwards (denoted as zonally resolved model).

The monthly mean SSTs of the central Arkona Sea of the years 1990-1998 were compared with the available solar radiation and air temperature of the region (Siegel et al., 1999b). The interannual variations in summer are strongly related. In spring the solar radiation forced the air temperature, and with a delay of one month followed the water temperature. In selected years, the air temperature also delayed by one month. In autumn, the solar radiation decreased first, and the delay of air and water temperature increased because of the heat storage by the water body. 

In Siegel et al. (2006), the regional differences were additionally discussed separately for the winter and summer months. In the northern Baltic Sea, particularly in Bothnian Bay, the SST every year in February is below 0°C and the water is often covered by ice. From the western part (Pl) to the northern Gotland Sea (P16), the mean SST is around 2°C. The warmest February was in 1990 with SST between 3 C and 4 C in the main parts. The coldest was in 1996 in the southwestern Baltic and in 2003 in the northern Baltic. The... 

In 1995-2000, annual mean SST decreased with a mean trend of about 0.1-0.3°C yr (the minimum trend was in the western region). Its estimated value for 2000 (about 11.8°C in the eastern Large Sea) appeared to be 0.6°C higher than the predicted one in [6] for this year with regard to the expected desiccation of the sea. 

Long-term variations of temperatm-e and salinity in the Bohai Sea are displayed in Fig. 1.8 (Lin et al., 2001). The annual mean SSS and SST of the Bohai Sea both show ascending trends dming 1960 1997. The linear trends were 0.074%o per year for SSS and 0.011 °C per year for SST respectively. The long-term variations of these annual means both showed climate-jump years or inflection years. 

The correlation coefficients between a 10 year monthly mean time series of volatilisation rates and SST, 1 Om wind speed and pollutant concentration are used to elucidate which of the parameters drives the volatilisation rate changes and causes the deviations from the long term mean. All of the parameters do not vary independently. Since both SST and wind speed influence the volatilisation rate in a nonlinear manner, it is not intuitive whether an increase in wind speed leads to an increase in volatilisation rate. A raise in wind speed that coincides with a decrease of the sea surface temperature can lead to a negative linear correlation coefficient between volatilisation rate and wind speed. For that reason the partial correlation coefficient is calculated in addition to the simple linear correlation coefficients. It explains the relation between a dependent and one or more independent parameters with reduced danger of spurious correlations due to the elimination of the influence of a third or fourth parameter, by holding it fixed. One important feature of the partial correlation coefficient is, that it is equal to the linear correlation coefficient if both variables... 

From Figs. 2a, 3a, and 4 it follows that, over the approximately 50-year-long period, the coldest winters in the Black Sea were observed in 1954, 1964, 1976, 1985, 1987, 1992, and 1993. Four of them occurred during the last 20 years. The lowest winter SST was registered in 1993. Starting from 1994, mean monthly winter temperatures have never decreased below 6.6 °C (Fig. 2a). High mean monthly and mean seasonal temperatures in winter were... 

Fig. 6 Six-month running mean of mean monthly ENSO (solid line) and NAO (dashed line) indices in the period 1950-2003 acquired from the Internet sites (http //www.cpc. NOAA.gov/data/indices/soi, http //www.cpc.NOAA.gov/data/teledoc). The circles indicate years with the anomalous winter SST values the black circles correspond to cold winters, the open circles to warm winters...

FIGURE 9.2 Interannual differences in the seasonal cycle of SST of the entire Baltic Sea in aU years of the investigation period compared to the total mean values. 

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